Asset allocation in a Bayesian copula-GARCH framework: An application to the ‘passive funds versus active funds’ problem

Long Kang ()
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Long Kang: Quantitative Risk Management, The Options Clearing Corporation, One North Wacker Drive

Journal of Asset Management, 2011, vol. 12, issue 1, No 4, 45-66

Abstract: Abstract We solve a one-period asset allocation problem with a Bayesian copula-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Investors invest among risk-free assets, a passive fund and an active fund, and maximize their expected utility. Posterior distributions of model parameters are drawn by the ‘Metropolis-within-Gibbs’ algorithm. Our results show significant percentage of holdings in active funds with different levels of risk aversion. With low risk aversion, Bayesian models yield similar portfolio weights and returns with non-Bayesian models. As risk aversion increases, however, Bayesian models imply more conservative weights in active funds and lead to significantly lower volatility of realized out-of-sample returns and utilities.

Keywords: copulas; Bayesian econometrics; asset allocation; Gibbs sampling (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (5)

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DOI: 10.1057/jam.2010.6

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